Abstract
This paper explores disparities in the effect of pollution on confirmed cases of Covid-19 based on counties’ socioeconomic and demographic characteristics. Using daily data on all US counties over the year 2020 and applying a rich panel data fixed effect model, we document that: 1) there are discernible social and demographic disparities in the spread of Covid-19. Blacks, low educated and poorer people are at higher risks of being infected by the new disease. 2) The criteria pollutants including Ozone, CO, PM10, and PM2.5 have the potential to accelerate the outbreak of the novel corona virus. 3) The disadvantaged population is more vulnerable to the effects of pollution on the spread of corona virus. Specifically, the effects of pollution on confirmed cases become larger for blacks, low educated, and counties with lower average wages in 2019. The results suggest that welfare programs during a global pandemic should be differentially distributed among families with different socioeconomic status since the effects of these programs in reducing the spread of the pandemic is different among subpopulations. This paper is the first study to evaluate the differential effects of pollution on the spread of novel corona virus across different subpopulations based on their socioeconomic status.
Highlights
The novel corona virus was observed initially in a small cluster in Wuhan, China in December 2019 and spread around the globe during the following year, and claimed about 1.85 million deaths in 2020 (Cnn 2020)
3) The disadvantaged population is more vulnerable to the effects of pollution on the spread of corona virus
The effects of pollution on confirmed cases become larger for blacks, low educated, and counties with lower average wages in 2019
Summary
The novel corona virus was observed initially in a small cluster in Wuhan, China in December 2019 and spread around the globe during the following year, and claimed about 1.85 million deaths in 2020 (Cnn 2020). Some studies point to the fact that there are disparities in the outbreak of the Covid-19 across occupations (McClure et al, 2020). Yang et al (2020) apply a negative binomial regression at a county-level dataset that covers data on Covid-19 cases up to June 13th and find that counties with a higher density of racial and ethnicity have higher confirmed cases. They show that this link is enhanced for counties with higher segregation between blacks and whites. NoghaniBehambari, Salari, et al (2020) examine the impacts of ambient air on the spread of Covid-19 across US counties
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